Explore the foundations of machine learning with TensorFlow, from setting up your workstation and key libraries to building basic models like linear and logistic regression, clustering, and neural networks.

Explore supervised, unsupervised, semi-supervised, and reinforcement learning, using labeled and unlabeled data, with natural language processing and computer vision applications like anomaly detection and clustering.

Explore how machine learning permeates everyday products, powering search, image and language processing, recommendations, and driverless technologies, while also noting its limits and data needs.

Explore how Jupyter enables interactive, open source notebooks built from cells for education and business reporting. Export these documents to multiple formats for versatile publishing.

Learn how to download, install, and configure Anaconda across Windows, Mac, and Linux, manage packages and environments, and understand its integration with bash startup files.

Explore options to set up machine learning workspaces across Windows and Linux, including cloud notebooks, hosted services, and collaboration-enabled environments for scalable training.

Explore third party libraries for machine learning, including numpy, pandas, matplotlib, and scikit-learn; learn data analysis, visualization, pre-processing, and simple neural networks for production-ready workflows.

Learn how to install and set up NumPy, create arrays from lists, and explore basic operations and multi-dimensional arrays, highlighting NumPy's speed over Python loops.

Explore creating, reshaping, and indexing arrays, generating random matrices, and computing max and min positions in a 5x5 array, with slicing and reshaping techniques.

Learn how universal functions apply across arrays to perform element-wise operations (add, subtract, multiply, divide, sin, log, exp) with broadcasting, improving performance over for loops.

Explore the pandas library for data analysis in machine learning workflows, install and activate your environment, load data from different sources, and transform datasets into analysis-ready formats.

Learn how to create pandas series from lists and dictionaries, assign custom indices, and perform element-wise operations like addition, noting that missing indices yield NaN.

Learn how data streams form data frames and series with a shared index, generate random data with normal distribution and seeds, and perform add, select, combine, and drop column operations.

Learn how to manipulate data frames by dropping rows and columns, using in-place operations, indexing by label or position, and applying conditional filters to access and subset data.

Explore using pandas to filter data with multiple conditions, apply boolean masks, and manage indexes including resetting, setting, and building multi-level indices for data frames.

Learn to handle missing data by creating a data frame from a dictionary, identifying NaN values, and using dropna to remove incomplete rows or columns.

apply group by on a data frame to summarize values by company, producing a new grouped structure. use in place options to modify the existing data stream and store results.

Group by company and directly compute mean, standard deviation, min, max, and count per group, and describe distribution with key percentiles.

Master pandas data manipulation by applying functions, managing columns and indices, sorting, and using group by, pivot tables, and import/export workflows in dataframes.

Learn import and export workflows by reading data from JSON, CSV, Excel, clipboard, SQL, and other sources, describe data frames, and export to Excel with controlled sheet names and index.

Learn the fundamentals of Python data visualization by exploring matplotlib, then seaborn and pandas visualization, with practical steps for setting up a Jupiter notebook environment and necessary libraries.

Matplotlib teaches you to plot data with customizable graphs, enabling visualization for machine learning debugging, feature understanding, and clear publication-ready visuals.

Learn an object-oriented plotting API: create a figure, add axes, and plot on them, control axis position (left, bottom, width, height) within 0 to 1, and set labels and titles.

Create a new dataframe from various data sources, manipulate columns and index, and visualize distributions with histograms and diverse plots using grammar of graphics and matplotlib styling.

Explore seaborn styling options, switch between default and dark backgrounds, and plot with data frames using bar, line, area, histograms, and scatter plots, highlighting correlation and transparency.

Explore how to visualize data with scatterplots using size and color encoding, box and violin plots, and density distributions in seaborne to support machine learning insights.

Seaborn offers a simple interface for statistical data visualization, enabling quick, advanced plots; the section is optional and covers installation and comparisons to other plotting libraries.

Explore seaborn for visualizing distributions with histograms and density plots, using tips data and built-in datasets to understand data first and adjust bins and styles.

Explore how a pairplot visualizes all numerical columns with histograms, revealing correlations useful for feature selection in linear regression across sex, smoker status, and dinner time in tips.

Explore categorical data with bar plots to compare average tips across lunch and dinner, by sex and other categories, and learn how to visualize counts and distributions.

Visualize distributions with a box plot, showing the box, whiskers, and outliers for numerical data across categories.

Explore how stripplot handles non categorical data on a single axis, then enhance readability with jitter, hue, and split options, and compare with violin, swarm, and bar plots for insight.

Learn to visualize data with heat maps and matrix-like plots, customize charts using a plotting library, and explore the iris dataset for classification across three species.

Learn to set up a conda environment for beginners, activate and deactivate environments, use a local environment on Windows, install scikit-learn (sikat), and start a notebook.

Explore scikit-learn, a versatile machine learning library with inbuilt models and data preprocessing tools, and learn the workflow from data collection to training and predictions, including a linear regression example.

Explore scikit-learn end-to-end: preprocess and visualize data, train and fine-tune models, and evaluate with training, validation, and test splits, using a housing price linear regression example.

Demonstrate loading and exploring the California housing dataset, inspecting feature names and data descriptions, and loading data from sklearn and CSV formats for preprocessing and visualization.

Visualize housing data with mean, standard deviation, quartiles, and correlations, including latitude and longitude. Learn data splitting into training and test sets, with optional validation, and histogram views.

Learn data visualization techniques to segment housing income data, create income categories, and interpret distributions with histograms for better housing price predictions.

Transform and clean data for machine learning by building reusable preprocessing steps, preparing train and test sets, selecting features to predict median housing value, and handling missing values.

Impute missing values in housing data using median, and transform data through a pre-processing pipeline to prepare numerical and categorical features for machine learning.

Replace missing values by imputing data and convert non-numeric features into numeric via one-hot or label encoding using pre-processing tools, simplifying model training.

apply label encoding for categorical labels and compare it with one-hot encoding, creating separate binary columns and using true/false indicators to avoid implying a correlation between numerical values in preprocessing.

Implement a custom ordinal encoder for housing data and use one encoding to create separate feature columns for each category, while leveraging a sparse matrix for efficient pre-processing.

Explore how to build a custom transformer and encoder, combine transformers in a pipeline, and handle missing values with mean, max, or min in housing data using scikit-learn.

Explore an end to end workflow for predicting housing values using linear regression, evaluating predictions with mean squared error, and testing alternative models like a decision tree regressor.

Revisits machine learning foundations, reviews workspace setup, visualization libraries like seaborn and matplotlib, and introduces TensorFlow—its advantages, alternatives, and basic operations with graph vs eager execution.

Master TensorFlow, a general-purpose open-source machine learning framework by Google, enabling graph-based computation with automatic differentiation and distributed across CPUs, GPUs, and other devices.

Explore basic tensor concepts, including scalars, vectors, and matrices, and learn how operations and computational graphs with constants, variables, and sessions drive machine learning workflows.

Explore basic tensor operations, including initializers, constants, scalars, vectors, and matrices, plus shaping, broadcasting, and matrix multiplication in a hands-on session.

Delve into basic TensorFlow ops, including session management, variable initialization, placeholders, and interactive versus eager execution, with practical examples of sess.run and feeding data.

Explore eager mode in TensorFlow, enabling immediate tensor execution without sessions or graphs. Apply basics to build a simple machine learning model for regression and classification.

Explore two simple linear models, linear regression and logistic regression, and master key concepts like labels, targets, features, and supervised learning to train and use models for predictions.

Implement a simple linear regression using the normal equation with matrix multiplication, deriving w and b from X and y and comparing with sklearn's linear regression.

Minimize the loss in a simple linear model by initializing with random values and applying gradient descent to update weights and bias on x and y.

Define the cost function as the average error between predicted and actual values across 17 samples, then minimize it with a gradient descent optimizer using a learning rate.

Explore how linear regression models learn from data by plotting input-output pairs, minimizing loss with a cost function and optimizers like gradient descent, and preparing for logistic regression.

Apply logistic regression to multiclass digit recognition on 28 by 28 images, using softmax to classify digits 0-9, with a 100-sample batch and a 0.01 learning rate.

Develop and evaluate a simple neural network for image classification, creating input ranges, applying softmax and cost optimization, and testing accuracy on a dataset.

Introduce neural networks, revisit simple problems such as classification, and discuss neural network basics as a building block for machine learning, with more detailed coverage.

Explore how neural networks learn from data by analyzing features using gradient descent to minimize a cost function, guided by learning rate, epochs, and initialization techniques.

Explore activation functions in neural networks, from shallow to deep architectures, and how nonlinearity enables the universal approximation theorem and reusable features.

Learn classification functions in neural networks, using 80/20 train/test splits, stochastic gradient descent with batches, and activation functions such as sigmoid and softmax for two-class and multi-class problems.

Learn to build a simple single-hidden-layer feedforward network for MNIST digit classification, using 784 input features, 10 output classes, softmax, and categorical cross-entropy.

This lecture demonstrates mnist digit classification using a simple neural network with dense layers. It trains, evaluates accuracy and loss, and discusses validation and deeper models with drop out.

Explore how machine learning and business intelligence analyze shipping and pricing for e-commerce, forecasting delivery times, correlating production forecasts with price, and identifying product trends from datasets.

Define and interpret the data dictionary and data points in an e-commerce inventory and orders dataset for machine learning forecasts of release and shipment timings with a three-month dummy dataset.

Define the data type and load libraries to prepare and read a dataset. Set up training and validation with a seed and partitioning, exploring numeric, date, and text fields.

Partition data into training and validation sets using indices, address validation index issues, and compute descriptive statistics to understand attribute types and distributions.

Explore density for numeric attributes, visualizing distributions with density blocks and box plots, and examine correlations through scatterplots to support descriptive statistics and feature selection.

Explore a method for train control using 10-fold cross-validation to select the best model for forecasting, comparing SVM, GNN, and clustering approaches and tuning parameters.

Explore demand forecasting with a machine learning example, using clustering for customer segmentation and data exploration to reveal spending distributions across regions and products.

Explore distribution attributes, visualize histograms, normalize features, and cluster spending across products to reveal total spending patterns.

Explore normalization and discretization to convert mixed data into standardized, discrete features, and learn ranking, quantiles, and dataset preparation through practical examples.

Learn to install clustering libraries, apply partition and hierarchical clustering, split data into two or three clusters, and visualize segmented results with heat maps.

Identify and set the correct function argument, assign values for color space and gamma, and run the function to generate a heat map visualization using the plots library.

Compare two tree diagrams by identifying identical and unique subtrees, leaves, and labeled branches. Visualize the results with heat maps and a diagram to interpret common subtrees, clusters, and structure.

Examine how dwelling type, house features, and condition indicators (exterior, foundation, basement, rooms) relate to the selling price in a housing market context.

Explore how features like the number of fire places, fireplace quality, garage type, and porch area feed a regression model to predict house selling price.

Import data and packages, inspect shapes, align train and test sets, and preprocess features by handling missing values and converting categorical columns for machine learning.

Set the index to the ID column and drop columns with over 20 percent missing values, then inspect unique values to distinguish numeric and categorical features.

Identify missing values, extract basement columns, and create frames to guide missing value imputation and distribution checks.

Impute missing values across dataset features, using the most frequent values and cross-column relationships to preserve data, including basement quality and garage features.

Transform and clean a dataset by replacing missing values, converting selected columns to objects, mapping month numbers to names, and distinguishing categorical from numeric features for machine learning readiness.

Change a data frame’s shape by expanding columns with dummy variables, then normalize features with a robust scaler to reduce outlier bias and scale values.

Explore gradient boosting to predict continuous sale price values, transform logged predictions back to the original scale, and evaluate model performance with cross-validation.

Explore preprocessing for loan data with logistic regression, using features such as loan amount, interest rate, employment, house ownership, and annual income, and perform a training-test split.

Explore logistic regression and stepwise logistic regression to interpret coefficients, standard errors, and significance in predicting loan status, using model summaries and standardized coefficients.

Explore predicting value with machine learning, assess model performance using cutoffs, accuracy, and probability across testing data, and tune thresholds to improve BI insights.

Explore profit and loss analysis by identifying profitable and losing users, preparing data with inputs such as transactions, account age, and frequency, and applying dea bp benchmarking efficiency methods.

Explore data envelopment analysis (dea) with input and profit and loss, computing efficiency using x1, x2 inputs and y1 output, and compare orientations to determine efficiencies.

Rank users with a line function, sort from smallest to highest rate, and classify into four risk-based segments. Explore how transactions and profits shape real-time rankings.

Explore rhs constraints in a machine learning workflow by modeling inputs and outputs, defining constraint directions, and configuring coefficients, then using a for loop to apply and visualize the constraints.

Learn to build a profit and loss report by applying directional and other constraints, computing efficiency via lambda-based calculations, and producing structured column reports.

Define input and output values, then build functional constraints and traces to model crisis scenarios. Test efficiency and correctness by running the constrained functions and refining values.

Derive profit efficiency from efficiency measures, classify users by efficiency scores, and segment customers to assess financial losses from fraud and align risk with inputs and outputs.

Explore Amazon machine learning as a fast, user-friendly cloud service that simplifies preprocessing, model building, and predictions, with multiple access options and region-based deployment, plus cost considerations.

Define the problem and decide when to use supervised learning, including classification or regression, then prepare data, train and evaluate models, and iteratively refine with new data and population shift.

Explore three ways to connect to data sources in AML, including Amazon storage service and an in-house cloud service; prepare input files, manage encoding, and chunk large files.

Create a robust data schema in aml by defining attributes, preprocessing data, and configuring training splits. Learn to handle missing values, random versus sequential splits, and data quality diagnostics.

Explore practical AML machine learning workflows, from preprocessing and simple classification or regression models to evaluating accuracy and cross-validation for batch predictions.

Develop a machine learning workflow to predict customer churn using a banking dataset with features like geography, age, balance, products, and salary, connecting to the console data source.

Explore a machine learning model example for data sources, including binary classification with logistic regression, training-validation splits, evaluation, and predictions across a mortgage data workflow.

Explore how to create and monitor a batch prediction, set an NFT destination, track progress, review logs, and access results via manifests and downloadable zip files.

Explore hands-on machine learning model predictions by analyzing customer churn and conversion risks, implement batch and real-time predictions, compare markets by accuracy, and deploy an endpoint for live scoring.

Manage machine learning objects through the dashboard by viewing, naming, deleting, and reusing batch predictions and evaluation objects; organize training and testing data and keep a clean object log.

Introduction to deep learning explains neural networks with multiple layers that learn from unstructured data such as images and videos, enabling nonlinear, step-by-step learning to solve classification tasks.

Explore forward propagation in a simple feedforward neural network, calculating outputs node by node and highlighting how activation functions decide which information passes to the next layer.

Learn activation functions from binary step and thresholding to non linear options, including sigmoid (0–1), tanh (−1 to 1), a negative-input inactivity rule, and softmax for classification probabilities.

Explore TensorFlow, the open source library for building neural networks with data flow graphs and sessions, covering MLP design, activation, loss, optimizer, and serving models.

Develop a simple neural network to predict financial distress using the Guardian dataset, handling features, categorical variables, and binary classification of distressed versus healthy companies.

Clean data, drop unused columns, and check for nulls; apply PCA to maximize explained variance, select top components, then split, train, and evaluate a model.

Import the neural network package, define a classifier, and configure a small-data setup. Use a sigmoid activation function to predict outcomes and achieve 96 percent accuracy on test data.

Implement with Keras to classify mushrooms as edible or poisonous using the 23-column dataset. Analyze features like color, bruises, smell, cap shape, and stalk properties to drive the model.

Identify two feature extraction approaches for sentiment analysis on airline seat comments: a dictionary-based unsupervised method and a Naive Bayes supervised classifier, built from a corpus.

Learn to replace contractions and clean text through a preprocessing pipeline that builds a word list, uses a dictionary, and removes punctuation with regex.

Remove stopwords to reveal meaningful tokens, then generate a word cloud and analyze word frequencies to describe journeys, using a workload package for processing.

Apply stemming and lemmatization to normalize text, using a dictionary-based approach with POS tagging to derive base forms. Utilize a library to map words to lemmas and synonyms.

Learn text pre-processing essentials by tokenizing, removing stopwords and punctuation, and creating feature vectors for model training using a bag-of-words approach with train-test splits.

Train a Naive Bayes classifier on text data using tf-idf and idf feature extraction, assess accuracy, and scale with large datasets for robust business intelligence applications.